Executive Summary: Employee Retention at Salifort Motors
Project Overview
- Developed a machine learning model to predict employee turnover at Salifort Motors
- Achieved 98% accuracy and 0.97 F1 score using Random Forest classifier
- Identified key factors contributing to employee turnover
Key Findings
- Employee satisfaction level is the strongest predictor of turnover
- Employees with high number of projects and long working hours are at risk
- Department and salary level have significant impact on retention
- Employees who stayed 3-4 years with no promotion show higher turnover
Recommendations
- Improve Satisfaction: Implement regular satisfaction surveys and feedback mechanisms
- Workload Balance: Review project allocation and working hours to prevent burnout
- Career Development: Create clearer promotion paths, especially for employees after 3 years
- Compensation Review: Evaluate salary structures in departments with high turnover
- Recognition Programs: Develop meaningful recognition for employee contributions
Implementation Timeline
- Immediate (0-3 months): Satisfaction surveys, workload review, recognition programs
- Short-term (3-6 months): Career development programs, compensation adjustments
- Long-term (6-12 months): Comprehensive retention strategy, leadership training